Day 1: Introduction to Python Programming & Machine Learning Fundamentals

• Introduction to Python programming and libraries (NumPy, Pandas, Matplotlib, Scikit-learn)
• Key Python concepts: Data structures, loops, functions, and libraries
• Introduction to machine learning: Types of learning (supervised, unsupervised, reinforcement)
• Data preprocessing for bioscience: Handling biological data, normalization, and missing data
• Feature engineering: Feature extraction and selection techniques
• Tools: Jupyter Notebooks, Pandas, Scikit-learn, Matplotlib

Day 2: Machine Learning Algorithms & Practical Applications in Biosciences

• Supervised learning algorithms: Linear regression, logistic regression, decision trees, and random forests
• Unsupervised learning algorithms: Clustering techniques (K-means, hierarchical)
• Support vector machines and neural networks for bioscience data analysis
• Applications of machine learning in bioscience research: Genomics, proteomics, and biomarker discovery
• Model evaluation techniques: Cross-validation, performance metrics (accuracy, precision, recall, F1 score)
• Tools: Scikit-learn, TensorFlow/Keras (for neural networks), Seaborn (for visualization)

Day 3: Advanced Machine Learning Techniques & Research-Grade Reporting

• Deep learning in biosciences: Introduction to deep neural networks (DNNs) for genomic data analysis
• Natural language processing (NLP) for biological text mining and literature analysis
• Model optimization: Hyperparameter tuning, grid search, and model comparison
• Research-grade reporting: Documenting findings, reproducible research, and best practices in machine learning for biosciences
• Real-world case studies in bioscience research with hands-on coding and model deployment
• Tools: TensorFlow, Keras, GridSearchCV, Jupyter Notebooks

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